Great question, and the one I struggled with a lot. When I finally saw the light it was like find the glasses I have misplaced three days ago. Here:
1. Efficacy of a test can only be judged against specific priors, not any possible circumstance.
2. Likewise, the goal of a test is to assist in making a particular decision, not all possible decisions.
Specifically 9% false positive rate is not useful for testing general population where effect size is expected to be on the order of 1-10%. However it is very useful in testing groups where expected effect size is much larger for example "all symptomatic people who came to hospital" at 50% or "all contacts of a known case" at 20%. The numbers are made up to illustrate the math.
Importantly our goal is not to detect all infected people ending the epidemic in XYZ days flat, it is to reduce the viral spread factor below 0.5, halving the epidemic every XYZ days. Thinking in absolutes is counter-productive.
Bearing all this in mind, the test can be useful. For example let's take a pool of people who are symptomatic, and say we expect 50% were in fact infected. The false positives (9% of the healthy 50% == 4.5%) will be outnumbered by true positives (* 99% of the infected 50% == 49%). So now you're looking at 49% vs 4.5%, a respectable 11:1 accuracy under the given priors. Not bad, for some applications.
And here's one good application: test a group of people from the pool of the currently symptomatic, quarantine them for two weeks, then release them into the wild without self-isolation rules. They will all be healthy due to quarantine, and 10/11 will be immune. If we keep releasing 91% immune groups of people into the general population the virus will die off.
* the antibody test I was referring to has 99% true positive and 9% false positive rates.